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Revealing new depths of information with indentation mapping of microstructures

  • Edoardo Rossi*
  • , Christophe Tromas
  • , Zhiying Liu
  • , Yu Zou
  • , Jeffrey M. Wheeler
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

114 Downloads (CityUHK Scholars)

Abstract

Nanoindentation is crucial in materials science for assessing mechanical properties in submicrometer volumes, and high-speed nanoindentation mapping has evolved it from a localized measurement technique into a scanning-probe-like approach for microstructures, delivering large-area, high-resolution mechanical property maps with more than 200,000 indents in hours. Such mapping enables direct imaging of hardness and modulus variations, phase boundaries, and local deformation behaviors in materials where heterogeneity governs mechanical performance. By correlating these mechanical maps with composition, orientation, and phase data from complementary analytical techniques, deep multidimensional data sets reveal the complex interplay between structure, processing, and properties. Such data sets increasingly demand advanced statistical clustering, machine learning, and deep learning for classification, trend extraction, and phase identification. Moving forward, high-speed nanoindentation is anticipated to operate under operando conditions and advanced mechanical modalities, offering new insights into interfacial deformation, anisotropic behavior, and the broader challenges of materials design and performance.

© The Author(s) 2025
Original languageEnglish
Pages (from-to)715-725
JournalMRS Bulletin
Volume50
Online published4 Jun 2025
DOIs
Publication statusPublished - Jun 2025

Funding

Open access funding provided by Università degli Studi Roma Tre within the CRUI-CARE Agreement. Y.Z. acknowledges partial funding from the Discovery Grants Program of the Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2018–05731]. E.R. gratefully acknowledges partial financial support from the European Commission, European project DigiCell, Grant Agreement No. 101135486.

Research Keywords

  • Nanoindentation
  • Hardness
  • Scanning electron microscopy (SEM)
  • Machine learning
  • Microstructure
  • Statistics/statistical methods

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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